From holism to compositionality: memes and the evolution of segmentation, syntax, and signification in music and language

Steven Mithen argues that language evolved from an antecedent he terms “Hmmmmm, [meaning it was] Holistic, manipulative, multi-modal, musical and mimetic”. Owing to certain innate and learned factors, a capacity for segmentation and cross-stream mapping in early Homo sapiens broke the continuous line of Hmmmmm, creating discrete replicated units which, with the initial support of Hmmmmm, eventually became the semantically freighted words of modern language. That which remained after what was a bifurcation of Hmmmmm arguably survived as music, existing as a sound stream segmented into discrete units, although one without the explicit and relatively fixed semantic content of language. All three types of utterance – the parent Hmmmmm, language, and music – are amenable to a memetic interpretation which applies Universal Darwinism to what are understood as language and musical memes. On the basis of Peter Carruthers’ distinction between ‘cognitivism’ and ‘communicativism’ in language, and William Calvin’s theories of cortical information encoding, a framework is hypothesized for the semantic and syntactic associations between, on the one hand, the sonic patterns of language memes (‘lexemes’) and of musical memes (‘musemes’) and, on the other hand, ‘mentalese’ conceptual structures, in Chomsky’s ‘Logical Form’ (LF)

Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models

Utilizing Computer Programming to Analyze Post-Tonal Music: A Segmentation and Contour Analysis of Twentieth-Century Music for Solo Flute

Two concepts will be synthesized in this dissertation: 1) the creation of accessible computer applications for melodic segmentation and contour reduction and 2) the application of segmentation and contour reduction to analyze twentieth-century post-tonal works for unaccompanied flute. Two analytical methodologies have been chosen: James Tenney and Larry Polanski\u27s Gestalt segmentation theory and Robert Schultz\u27s refinement of Robert Morris\u27s contour reduction algorithm. The investigation also utilizes Robert Schultz\u27s concept of diachronic-transformational analysis in conjunction with contour reduction. While both segmentation and contour reduction are invaluable analytical tools, they are meticulous and time-consuming processes. Computer implementation of these algorithmic procedures produces quick and accurate results while reducing analyst fatigue and human error. Microsoft Excel is used to complete melodic segmentation. Java programming language is used to create a contour reduction application. Each implementation greatly reduces the time needed to segment and analyze a melody. Computer programming is combined with pitch class set analysis to produce informed and expressive musical interpretations

AI2D-RST : A multimodal corpus of 1000 primary school science diagrams

This article introduces AI2D-RST, a multimodal corpus of 1000 English-language diagrams that represent topics in primary school natural sciences, such as food webs, life cycles, moon phases and human physiology. The corpus is based on the Allen Institute for Artificial Intelligence Diagrams (AI2D) dataset, a collection of diagrams with crowdsourced descriptions, which was originally developed to support research on automatic diagram understanding and visual question answering. Building on the segmentation of diagram layouts in AI2D, the AI2D-RST corpus presents a new multi-layer annotation schema that provides a rich description of their multimodal structure. Annotated by trained experts, the layers describe (1) the grouping of diagram elements into perceptual units, (2) the connections set up by diagrammatic elements such as arrows and lines, and (3) the discourse relations between diagram elements, which are described using Rhetorical Structure Theory (RST). Each annotation layer in AI2D-RST is represented using a graph. The corpus is freely available for research and teaching.Peer reviewe